Debmalya Sanyal
1, Kingshuk Bhattacharjee
2, Pratik Das
31Department of Endocrinology, KPC Medical College, Jadavpur, Kolkata, West Bengal, India
2SJJT University, Rajasthan, India
3Department of Nephrology, Rabindranath Tagore International Institute of Cardiac Sciences, EM Bypass, Kolkata, West Bengal, India
A new scoring system to predict
the incidence of new onset diabetes after transplantation (NODAT)
ABSTRACT
Background. We performed this study to develop a new scoring system to stratify different levels of risk of developing new onset diabetes after trans- plantation (NODAT) in patients who underwent renal transplantation. Many prognostic variables have been previously described but few efforts have been made to group them in order to enhance their individual predictive power.
Material and methods. In a first phase, 100 patients were prospectively analysed to determine which factors were significantly associated with the deve- lopment of NODAT. A risk score ranging from 0 to 10 points was developed using a multivariate analysis.
In a second phase, such score was validated in a new sample of 100 patients.
Results. BMI ≥ 23.5 kg/m2, age ≥ 38.5 years, fasting blood sugar at 1st post-operative day ≥ 159.5 mg/dL, fasting blood sugar at 5th post-operative day ≥ 122.5 mg/dL and HOMA-IR ≥ 2.5 were found as independent prognostic variables. A clear distinction was shown among categories of low, intermediate and high risk, defined according to the risk score.
Conclusion. This new scoring framework is basic and simple to accomplish. It permits a generally excellent stratification of risk of developing NODAT in patients
undergoing renal transplantation. They might be sepa- rated in three risk stratification cohorts, which could be of help in early identification of NODAT. (Clin Diabetol 2020; 9; 4: 226–232)
Key words: NODAT, risk score, India, renal transplantation
Introduction
New‑onset diabetes after transplantation (NODAT) refers to diabetes that occurs in previously non‑diabetic persons after solid‑organ transplantation, according to International consensus guidelines published in 2003 [1, 2]. There are many risk factors of NODAT. Some risk factors are the same as in general risk factors for diabetes mellitus (DM), while some others are specific to transplantation.
Some common risk factors include age, obesity, African‑American and Hispanic [3–6]. In addition, some risk factors are unique to the transplant population.
These include specific agents used for immunosup‑
pression, human leukocyte antigen mismatch, donor sex and type of underlying renal disease [7]. Impaired glucose tolerance prior to transplant [8] and hypergly‑
caemia in the immediate perioperative period [9, 10]
may identify patients at higher risk for the develop‑
ment of NODAT. There is paucity of data with regards to the development of risk scores for the development of new onset diabetes after transplantation (NODAT) from South‑East Asian population. Although data from western population are available, we intended to de‑
velop the same for our population which is place of resi‑
dence for almost one-fifth of the world’s population.
Furthermore, the pre‑transplant and peri‑transplant
Address for correspondence:
Dr Debmalya Sanyal Department of Endocrinology KPC Medical College, Jadavpur, Kolkata West Bengal, India
e‑mail: drdebmalyasanyal@gmail.com Clinical Diabetology 2020, 9, 4, 226–232 DOI: 10.5603/DK.2020.0024
Received: 01.03.2020 Accepted: 04.05.2020
risk factors in our study population also differ con‑
siderably from the western population which calls for development of population specific predictive model for development of NODAT in the intended population.
We conducted the present study to test the prognostic value of a combination of such risk factors resulting in a prospectively designed score that could be capable of making a clear distinction of different clinical outcomes with regards to development of NODAT applied to patients coming to hospital for renal transplantation.
With that purpose we chose the most widely available prognostic variables that, in our model, provided the best independent information for the development of NODAT. The new score was applied in another cohort of patients consecutively admitted to renal transplant units who were not enrolled in trials of therapeutic interventions.
Material and methods Study population
This was a single‑centred prospective study of 200 subjects who underwent renal transplantation over a period of four years in a tertiary care centre in eastern India.
The inclusion criteria comprised of adult subjects with end stage renal disease who underwent live donor kidney transplantation and absence of diabetes prior to kidney transplantation, defined according to the American Diabetes Association (ADA) guideline. None of these patients were on any oral hypoglycaemic agents or insulin prior to kidney transplantation. All patients received their allograft from a living (related or unrelated) donor. All subjects received standard immunosuppressive medications that included triple immunosuppressive medications namely tacrolimus, mycophenolate mofetil or mycophenolate sodium and steroids with induction (ATG). Immunosuppres‑
sive therapy comprised tacrolimus (initiation dose of 0.15 mg/kg) (with target blood level of tacrolimus 10–15 ng/ml between 1st 3month, 5–10 ng/ml between 3rd to 6th month and 3–5 ng/ml in 6th to 12th month), prednisolone (20 mg/d) (with gradual tapering of dose 2.5 mg per month with target of 5 mg at the end of 6th month), and or mycophenolate mofetil (1.5 g/d).
Previous studies in NODAT have modified the immuno‑
suppressive regimen to prevent NODAT development.
But ADA recommends that immunosuppressive regi‑
mens associated with best patient and graft survival should be used, irrespective of post‑transplantation diabetes mellitus risk [11]. Subjects who were capable of understanding the study and gave informed written consent for study participation were only included.
Patients with a diagnosis of DM prior to kidney trans‑
plantation based on ADA criteria, for diagnosis of DM [12], or those receiving anti‑diabetic medications or those who were not capable of providing consent were excluded from the study.
‘Prediabetes’ in our study was defined according to ADA 2016 guidelines as HbA1c value 5.7–6.4%. Those who are non‑diabetic and underwent renal transplan‑
tation are further evaluated for the development of NODAT during 1‑year post‑transplantation follow‑up.
Post‑transplant follow‑up done on weekly basis for 1st month, every 15th day from 1st month to 3rd month, monthly from 3rd month to 12th month. Each transplant patient was followed up for 1‑year post‑transplant or for 6 months post‑development of NODAT, whichever is later. NODAT was defined according to standard ADA criteria provided the patient was receiving therapy (oral hypoglycaemic drugs or insulin) at 3 months post‑
transplant. Immediate posttransplant hyperglycaemia was defined as a random blood sugar (RBS) ≥ 200 mg/dL [1] or requirement of insulin on > 2 days whereas the patient was of dextrose-containing fluid infusions (usu‑
ally from the 4th postoperative day).
In addition to routine transplant workup, pretrans‑
plant BMI, family history of DM, HbA1c, fasting insulin level, fasting C‑peptide level, serology for hepatitis B, C and serum magnesium level were evaluated in all patients 2 days prior to transplant. Pre‑operative insulin resistance (HOMA‑IR), insulin sensitivity (HOMA‑S) beta‑cell function (HOMA‑B and C‑peptide levels) were assessed. All the above pre‑transplant variables were further compared between NODAT and non‑NODAT subjects at the end of the study to assess their strength of association.
Data management and statistical analysis Descriptive statistics was analysed with SPSS ver‑
sion 17.0 software for windows. In order to develop a risk score, all demographic, clinical, and biochemical variables were routinely collected. Continuous vari‑
ables were presented as mean ± SD and analysed by unpaired t test. Categorical variables were expressed as frequencies and percentages. Nominal categorical data between the groups were compared using Chi‑square test or Fischer’s exact test as appropriate. A p value of
< 0.05 was considered to be statistically significant.
Univariate was analysis done to evaluate odds ratio of various parameters associated with increased risk of NODAT among study population.
Every variable resulting in a p value < 0.01 in the univariate model was entered into a multiple logistic regression analysis to determine which were indepen‑
dently related to the end‑points.
The predictive accuracy of the multivariate model was evaluated using the C statistic, an index that
reflects the area under the receiver operating charac‑
teristic curve.
The odds ratio (OR) values obtained in the mul‑
tivariate analysis were used to develop the scoring system in the following way: if the OR was between 1 and 1.9, one point was adjudicated; two points if it was between 2 and 2.9; three points between 3 and 3.9 and four points if it exceeded the last value.
Once the risk score was developed, we conduc‑
ted a validation phase to assess its prognostic accu‑
racy in a prospectively collected new sample of patients.
The overall predictive ability of the risk score was then assessed with the C statistic and compared with that obtained from the multivariate model of the de‑
velopment phase.
Table 1. Baseline characteristics of the subjects who developed NODAT in development phase and validation phase Development phase (n = 100) Validation phase (n = 100)
Age (years), mean (SD) 45.2 (10.93) 46 (11)
Family H/O diabetes mellitus, n (%) 25 (25) 32 (32)
BMI [kg/m2], mean (SD) 22.62 (4.03) 23.15 (5.77)
Hepatitis B infection, n (%) 3 (3) 3 (3)
Hepatitis C infection, n (%) 3 (3) 3 (3)
Autosomal dominant polycystic kidney disease, n (%) 4 (4) 3 (3)
Mean magnesium levels [mEq/L], mean (SD) 1.84 (0.51) 1.88 (0.37)
Mean total cholesterol levels [mg/dL], mean (SD) 139.41 (35.01) 136 (21)
Mean triglyceride levels [mg/dL], mean (SD) 84.02 (64.52) 89.5 (57.21)
Pre‑operative HbA1c > 5.7%, n (%) 14 (14) 16 (16)
Pre‑operative HbA1c (%), mean (SD) 5.34 (0.16) 5.12 (0.1)
ABO compatibility transplant, n (%) 94 (94) 96 (96)
HOMA‑IR, mean (SD) 1.87 (1.08) 1.84 (1.11)
HOMA‑S, mean (SD) 79.35 (48.07) 84.57 (40.18)
HOMA‑beta cell function, mean (SD) 64.14 (3.64) 68.23 (4.21)
C‑peptide level, mean (SD) 11.06 (5.09) 10.21 (6.21)
Table 2. The results of the univariate analysis
Variable OR 95% CI P value
Age (years), mean ± SD 1.084 1.033–1.138 0.004
Family H/O diabetes, n (%) 1.133 1.013–1.890 < 0.001
Fasting blood sugar‑day 1 [mg/dL] 2.032 1.412–3.785 < 0.001
BMI [kg/m2], mean ± SD 1.363 1.178–1.577 < 0.001
Hepatitis B infection, n (%) 7.4 0.638–85.81 0.28
Hepatitis C infection, n (%) 7.4 0.638–85.81 0.28
Fasting blood sugar‑day 5, mean ± SD 9.28 5.413–16.519 < 0.001
Autosomal dominant polycystic kidney disease 1.001 0.856–1.087 0.242
Mean magnesium level [mEq/L], mean ± SD 0.780 0.315–1.930 0.305
Mean total cholesterol level [mg/dL], mean ± SD 1.015 0.997–1.032 0.120
Mean triglycerides level [mg/dL], mean ± SD 1.008 0.998–1.018 0.066
Pre‑operative HbA1c (%) > 5.7% 2.315 1.389–2.561 < 0.001
Pre‑operative HbA1c (%), mean ± SD 1.057 1.029–1.185 0.001
ABO compatibility transplant 0.135 0.023–0.792 < 0.001
HOMA‑IR 0.987 0.932–0.998 0.001
HOMA‑S 0.957 0.921–0.997 < 0.001
HOMA‑beta cell function 0.956 0.901–0.978 0.001
C‑peptide level 1.987 1.057–2.184 < 0.001
Results
Score development phase
One hundred patients were prospectively included in this phase. Among the 100 subjects included in the analysis, 24 patients (19 males and 5 females) developed NODAT during 1 year of follow‑up after transplantation.
Eighteen variables were included in the uni‑
variate analysis. We included the variables which had a p value less than 0.010 for multivariate analysis.
Hence, we included 14 variables in the multiple regres‑
sion model and only BMI, age, fasting blood sugar at 1st and 5th post‑operative day and HOMA‑IR were found as independent prognostic variables of NODAT. The C Figure 1. ROC of the significant predictor variables
0.0 0.0
0.2 0.2
0.4 0.4
0.6 0.6
0.8 0.8
1.0 1.0
0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0
1-Specicity 1-Specicity
ROC curve for age in predicting NODAT ROC curve of BMI for predicting NODAT
Sensitivity Sensitivity
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
1-Specicity
ROC curve for FBS in predicting NODAT
Sensitivity
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
1-Specicity
ROC curve — FBS on 5 day PO in predicting NODATth
Sensitivity
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
1-Specicity
ROC curve of HOMA-IR in predicting NODAT
Sensitivity
statistic for the multivariable model was 0.79 (95% CI 0.74–0.89).
Development of scoring system
Therefore, according to the OR obtained, the scor‑
ing system was established as follows:
— age ≥ 38.5 years (OR = 1.231): 1 point;
— BMI ≥ 23.5 kg/m2 (OR = 2.103): 2 points;
— HOMA‑IR ≥ 2.5 (OR = 4.062): 4 points;
— FBS on 1st POD ≥ 159.5 (OR = 1.011): 1 point, and
— FBS 5th day POD ≥ 122.5 (OR = 2.082): 2 points.
As the highest possible score was 10 points, we divided it in tertiles so that we could assign each patient to one of three categories according to the score sum value: low‑risk when it was 0 to 2, intermediate‑risk when it was 3 to 6 and high‑risk when it was 7 to 10.
Validation phase
One hundred patients entered this phase of the study. Baseline characteristics were similar to the first except for a slightly higher prevalence of family history of diabetes in the validation cohort. The incidence of NODAT was similar in the development phase (24%) and validation phase (26%).
The incidence of NODAT occurred in 8.7% of low risk patients, 34.62% of intermediate risk and 46.43%
of high risk patients (OR for high vs. low risk: 5.34, 95%
CI 2.9–11.8, P < 0.001; OR for high vs. intermediate risk: 1.34, 95% CI 0.69–9.82, P = 0.044; OR for inter‑
mediate vs. low risk: 3.98, 95% CI 2.1–6.7, P = 0.001).
Predictive power of the score, as assessed by the C statistic was 0.78 (95% CI 0.67–0.88), similar to that found for the multivariate model.
Discussion
Although many demographic, clinical and bio‑
chemical markers have been clearly shown to correlate with development of NODAT, few efforts have been made to group them in order to improve their individual predictive power.
The scoring system proposed here is quite simple to implement and has a good ability to discriminate risk ac‑
cording to the C‑statistic value. All the information needed in our study to predict NODAT namely age, BMI, HOMA‑IR and FBS are easily available. All of these are non‑expensive and most importantly, they have a very good prognostic value. We divided the population studied into three groups:
low, intermediate and high risk, which is a common practice among clinicians regarding many chronic diseases.
In the present study, age > 38.5 years was found to be a significant predictor for the development of NO‑
DAT. Increasing age is associated with increased risk for NODAT aspecially over the age of 40 years [6, 10, 12].
A study by Crosio et al. in 2078 patients showed that patients older than 45 years were 2.9 times increased risk of developing diabetes [13]. Every 10‑year increase in age leads to 1.5‑fold increased risk of diabetes [14].
In our study, pre‑transplant BMI > 23.5 kg/m2 is found to be significant prognostic variable for develop‑
ment of NODAT. Obesity independently correlates with the development of NODAT [3–5, 15, 16]. An analysis of 15,309 patients using the Organ Procurement and Transplant Network/United Network for Organ Sharing (OPTN/UNOS) database found that the risk of NODAT increased 1.4‑fold for those with a BMI of 25–30 and nearly doubled if the BMI was > 30 [17]. It remains unclear whether weight gain after transplantation is Table 4. Outcomes according to risk categorization in the validation phase
Low risk (n = 46) Intermediate risk (n = 26) High risk (n = 28)
Incidence of NODAT 4 (8.7) 9 (34.62) 13 (46.43)
OR for high vs. low risk 5.34, 95% CI 2.9–11.8
OR for high vs. intermediate risk 1.34, 95% CI 1.169–9.82
OR for intermediate vs. low risk 3.98, 95% CI 2.1–6.7
Table 3. Results of the multivariate analysis
B — coefficient C — statistics Best cut-off Sensitivity Specificity OR, 95% CI
Age 0.703 0.76 38.5 83.33 71.4 1.231 (1.133–1.938)
BMI 0.821 0.80 23.5 75.6 84.2 2.103 (1.278–2.878)
HOMA‑IR 1.439 0.81 2.5 74.2 78.8 4.062 (2.79–5.81)
FBS‑1st day POD 0.694 0.72 159.5 77.6 82.9 1.011 (1.002–1.018)
FBS‑5th POD 0.936 0.84 122.50 79 92.1 2.082 (1.037–4.088)
the cause, however one study suggested pre‑transplant weight increases the risk for NODAT [18].
Midtvedt et al. using hyperinsulinaemia euglycemic clamps found insulin resistance as a common denomi‑
nator of KTRs with NODAT and IGT [19]. In study done by Bayes et al. NODAT patients showed significantly higher pre‑transplant plasma insulin concentrations and HOMA‑IR index compared to non‑NODAT patients [20]. We found HOMA‑IR of more than 2.5 had an OR of 4.062 for NODAT development.
Patients with post‑transplant hyperglycemia in our study had a fourfold higher risk of developing NODAT.
Similar results were seen in the study by Chakkera et al.
in 200 posttransplant patients in Arizona [21]. A study from Chile reported 5.4‑fold higher risk of developing diabetes in patients with early hyperglycemia [22].
A French study found first post-transplantation capil‑
lary blood glucose and fasting blood glucose on 1st day tended to be higher in patients who developed diabetes 3 months later [23]. They reported maximum hyperglycaemia on the first post-operative day which decreased gradually during first 4 days of transplan‑
tation probably related to decrease in corticosteroid dosages and reduction in insulin resistance due to resolution of uraemia. A Belgian study demonstrated that a normal OGTT on the 5th post‑operative day was associated with a significantly decreased risk of NODAT at 3 months [24]. We also found that persistent post‑
operative day 5th FBS to have a higher odd of NODAT development compared to day 1 FBS. The significant risk of NODAT posed by posttransplant hyperglycaemia makes it prudent to follow up these patients more dili‑
gently and are likely to benefit from intensive glucose monitoring. Based on the available evidence, NODAT cannot be efficiently prevented by tailored immuno‑
suppression alone without compromising kidney graft survival. In the TIP‑study, early use of basal insulin in the immediate post‑transplantation period lower odds of NODAT by 73% throughout 1 year of follow‑up [25].
Only one previous study in predominantly white transplant recipients described a pretransplant predic‑
tive risk model for NODAT using seven pretransplant variables (age ≥ 50 years, planned use of maintenance corticosteroids; use of gout medicine; BMI ≥ 30 kg/m2; fasting glucose ≥ 100 mg/dL; fasting triglycerides
≥ 200 mg/dL; and family history of type 2 diabetes) [26]. But they did not consider peri‑transplant risk factors like immediate post‑operative hyperglycemia which are strongly associated with NODAT. Moreover, there is variability in the use of gout medication and corticosteroids. In our study, be had a standard im‑
munosuppressive regimen for best transplant outcome in all patients and according to OR obtained, scoring
system using five variables (age ≥ 38.5 years; BMI ≥ 23.5 kg/m2; HOMA‑IR ≥ 2.5; FBS on 1st POD ≥ 159.5;
FBS 5th day POD ≥ 122.5) was established. There is al‑
ways a concern of higher mortality in the person who develops NODAT which warrants adaptation of risk scores in routine clinical setting by transplant physi‑
cians. A study by Cooper et al. with 266 participants undergoing kidney transplantation found the age and sex adjusted mortality to be 1.69 times higher among patients with NODAT versus those without NODAT, hazard ratio 2.69 (95% CI 1.04–7.01) [27]. Cosio et al.
described two fold increase in mortality with NODAT compared to nontransplant recipients, which was equal to that of pretransplant diabetes and independent of other factors known to reduce survival [28].
Conclusion
The morbidity and mortality associated with NO‑
DAT makes it prudent to a identify risk factors and develop a risk score for early detection of NODAT and stratify effective strategies for prevention and intensive treatment in resource‑limited setting wherein extensive monitoring in all patients is expensive. This new scoring framework is basic and simple to accomplish. It permits a generally excellent stratification of risk of developing NODAT in patients undergoing renal transplantation.
They might be separated in three risk stratification cohorts, which could be of help in the decision‑making process. Our findings should be tested in a larger co‑
hort of patients in order to suggest clinical strategies based on them. If these data were confirmed, a highly aggressive approach as per recent evidences can be recommended in high risk and intermediate risk pa‑
tients and a more conservative one could be reserved for the low risk group.
Conflict of interest
The authors declare to have no conflict of interest.
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